mlsanityData to 7 Oct 2026

LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers

arXiv 2403.15529 ↗First posted 22 Mar 2024cs.CL cs.AI cs.IRcited by 16

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Abstract (arXiv)

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers. We compile a dataset called textbftextitLimGen, encompassing 4068 research papers and their associated limitations from the ACL anthology. We investigate several approaches to harness large language models (LLMs) for producing suggestive limitations, by thoroughly examining the related challenges, practical insights, and potential opportunities. Our LimGen dataset and code can be accessed at urlhttps://github.com/arbmf/LimGen.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 22 Mar 2024.

Builds on

25 of 33 references.

References · 25

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  6. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  7. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
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  9. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
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  11. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  12. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
13 more
  1. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
  2. 2020TLDR: Extreme Summarization of Scientific Documentscited by 136
  3. 2023ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewingcited by 118
  4. 2023Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Clustercited by 106
  5. 2019ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networkscited by 73
  6. 2023TIGERScore: Towards Building Explainable Metric for All Text Generation Taskscited by 69
  7. 2021Can We Automate Scientific Reviewing?cited by 63
  8. 2017A Supervised Approach to Extractive Summarisation of Scientific Paperscited by 33
  9. 2021Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documentscited by 33
  10. 2019TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talkscited by 20
  11. 2023Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Surveycited by 18
  12. 2022CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervisioncited by 15
  13. 2020What's New? Summarizing Contributions in Scientific Literaturecited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 7.

16 papers.

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  1. 2024OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviewscited by 63
  2. 2025Large language models for automated scholarly paper review: A surveycited by 59
  3. 2025LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitationscited by 12
  4. 2026Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Futurecited by 6
  5. 2026RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generationcited by 5
  6. 2024No Size Fits All: The Perils and Pitfalls of Leveraging LLMs Vary with Company Sizecited by 3
  7. 2025Graph-Guided Passage Retrieval for Author-Centric Structured Feedbackcited by 3
  8. 2025BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Textcited by 3
  9. 2025Multi-Agent LLMs for Generating Research Limitationscited by 2
  10. 2026When AI reviews science: Can we trust the referee?cited by 2
  11. 2026FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Researchcited by 1
  12. 2026LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challengescited by 1
4 more
  1. 2024CCSBench: Evaluating Compositional Controllability in LLMs for Scientific Document Summarizationcited by 0
  2. 2025Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGIcited by 0
  3. 2026Judgment-Grounded Expansion for Peer Review Generationcited by 0
  4. 2026What Limits Us? Analyzing Self-Reported Limitations in NLP Researchcited by 0